Nerfbusters: Removing Ghostly Artifacts from Casually Captured NeRFs

Casually captured Neural Radiance Fields (NeRFs) suffer from artifacts such as floaters or flawed geometry when rendered outside the camera trajectory. Existing evaluation protocols often do not capture these effects, since they usually only assess image quality at every 8th frame of the training capture. To push forward progress in novel-view synthesis, we propose a new dataset and evaluation procedure, where two camera trajectories are recorded of the scene: one used for training, and the other for evaluation. In this more challenging in-the-wild setting, we find that existing hand-crafted regularizers do not remove floaters nor improve scene geometry. Thus, we propose a 3D diffusion-based method that leverages local 3D priors and a novel density-based score distillation sampling loss to discourage artifacts during NeRF optimization. We show that this data-driven prior removes floaters and improves scene geometry for casual captures.Comment: ICCV 2023, project page: https://ethanweber.me/nerfbuster

Distribution, host plants and natural enemies of sugar beet root aphid (Pemphigus fuscicornis) In Slovakia

During 2003-2004, field surveys were realized to observe the distribution of sugar beet aphid, Pemphigus fuscicornis (K o c h) (Sternorrhyncha Pemphigidae) in southwestern Slovakia. The research was carried out at 60 different localities with altitudes 112-220 m a. s. l. Sugar beet root aphid was recorded at 30 localities. The aphid was recorded in Slovakia for the first time, but its occurrence was predicted and symptoms and harmfulness overlooked by now. The presence of P. fuscicornis was investigated on roots of various plants from Chenopodiaceae. The most important host plants were various species of lambsquarters (above all Chenopodium album). Furthermore sugar beet (Beta vulgaris provar. altissima), red beet (B. vulgaris provar. conditiva) and oraches (Atriplex spp) act as host plants. Infestation of sugar beet by P. fuscicornis never exceeded 5% at single locality in Slovakia. Dry and warm weather create presumptions for strong harmfulness. In Slovakia, Chenopodium album is a very important indicator of sugar beet aphid presence allowing evaluation of control requirements. During the study, the larvae of Thaumatomyia glabra (Diptera: Chloropidae) were detected as important natural enemies of sugar beet aphid. The species occurred at each location evaluated

NerfAcc: Efficient Sampling Accelerates NeRFs

Optimizing and rendering Neural Radiance Fields is computationally expensive due to the vast number of samples required by volume rendering. Recent works have included alternative sampling approaches to help accelerate their methods, however, they are often not the focus of the work. In this paper, we investigate and compare multiple sampling approaches and demonstrate that improved sampling is generally applicable across NeRF variants under an unified concept of transmittance estimator. To facilitate future experiments, we develop NerfAcc, a Python toolbox that provides flexible APIs for incorporating advanced sampling methods into NeRF related methods. We demonstrate its flexibility by showing that it can reduce the training time of several recent NeRF methods by 1.5x to 20x with minimal modifications to the existing codebase. Additionally, highly customized NeRFs, such as Instant-NGP, can be implemented in native PyTorch using NerfAcc.Comment: Website: https://www.nerfacc.co

Instruct-NeRF2NeRF: Editing 3D Scenes with Instructions

We propose a method for editing NeRF scenes with text-instructions. Given a NeRF of a scene and the collection of images used to reconstruct it, our method uses an image-conditioned diffusion model (InstructPix2Pix) to iteratively edit the input images while optimizing the underlying scene, resulting in an optimized 3D scene that respects the edit instruction. We demonstrate that our proposed method is able to edit large-scale, real-world scenes, and is able to accomplish more realistic, targeted edits than prior work.Comment: Project website: https://instruct-nerf2nerf.github.io; v1. Revisions to related work and discussio

GARField: Group Anything with Radiance Fields

Grouping is inherently ambiguous due to the multiple levels of granularity in which one can decompose a scene -- should the wheels of an excavator be considered separate or part of the whole? We present Group Anything with Radiance Fields (GARField), an approach for decomposing 3D scenes into a hierarchy of semantically meaningful groups from posed image inputs. To do this we embrace group ambiguity through physical scale: by optimizing a scale-conditioned 3D affinity feature field, a point in the world can belong to different groups of different sizes. We optimize this field from a set of 2D masks provided by Segment Anything (SAM) in a way that respects coarse-to-fine hierarchy, using scale to consistently fuse conflicting masks from different viewpoints. From this field we can derive a hierarchy of possible groupings via automatic tree construction or user interaction. We evaluate GARField on a variety of in-the-wild scenes and find it effectively extracts groups at many levels: clusters of objects, objects, and various subparts. GARField inherently represents multi-view consistent groupings and produces higher fidelity groups than the input SAM masks. GARField's hierarchical grouping could have exciting downstream applications such as 3D asset extraction or dynamic scene understanding. See the project website at https://www.garfield.studio/Comment: Project site: https://www.garfield.studio/ First three authors contributed equall